Technical Field
The present invention relates to deriving disparity of an object based on a reference image and a comparison image each obtained by capturing an image of the same object.
Background Art
A range-finding method using disparity information is conventionally known, in which disparity of an object observed by a stereo camera is derived by stereo imaging, and a disparity value indicating this disparity is used to measure the distance from the stereo camera to the object based on the principle of triangulation. With this range-finding method, for example, the distance between two automobiles or between an automobile and obstacles can be measured and utilized for preventing automobile collisions. The range-finding method may be also referred to as the distance measurement method.
Specifically, a stereo matching process is employed for deriving disparity. In the stereo matching process, a reference image is captured by one camera of a stereo camera, composed of two cameras, and a comparison image is captured by the other camera of the stereo camera. Then, by successively shifting a plurality of candidate corresponding pixels in the comparison image relative to one reference pixel set in the reference image, a position of a corresponding pixel having an image signal that is the most similar to an image signal of the reference pixel set in the reference image is identified to derive a disparity value between the reference image and the comparison image. Typically, luminance values of image signals obtained by the two cameras are compared to compute “cost” (hereinafter, cost means “dissimilarity”) of the compared luminance values, with which a position of a pixel having the smallest cost is identified. Further, the stereo matching process can employ a block matching process to prevent mismatching, in which luminance at edges in an image where luminance changes greatly are compared as disclosed in JP-2006-090896-A.
However, for areas having weak texture (i.e., an area where the magnitude of luminance change of an object is weak) and the features to be extracted are themselves diminished, edge detection may not be effective.
In view of this ineffective edge detection, a method that derives more accurate disparity for an object having weak texture is proposed as disclosed in JP-2012-181142-A. In this method, the cost of one reference pixel in a reference image and also the costs of other pixels around the one reference pixel are aggregated to derive disparity for an object having weak texture. With this method, disparity of the entirety of the object can be derived and used not only for range-finding but also classification of the object (e.g., whether the object is a sign or an automobile).
When the method disclosed in JP-2012-181142-A is applied for capturing a scene including an object having weak texture and an object having strong texture that may exist at relatively far positions with each other, disparity can be derived for the weak-texture object and the strong-texture object on a reference image. However, disparity at one area composed of pixels corresponding to the strong-texture object may affect disparity at another area composed of pixels corresponding to the weak-texture object. Therefore, disparity detection cannot be performed with high precision.